Using fuzzy logic to learn abstract policies in large-scale multiagent reinforcement learning
J Li, H Shi, KS Hwang - IEEE Transactions on Fuzzy Systems, 2022 - ieeexplore.ieee.org
Large-scale multiagent reinforcement learning requires huge computation and space costs,
and the too-long execution process makes it hard to train policies for agents. This work …
and the too-long execution process makes it hard to train policies for agents. This work …
A multi-agent reinforcement learning method with curriculum transfer for large-scale dynamic traffic signal control
X Li, J Li, H Shi - Applied Intelligence, 2023 - Springer
Using reinforcement learning to control traffic signal systems has been discussed in recent
years, but most works focused on simple scenarios such as a single crossroads, and the …
years, but most works focused on simple scenarios such as a single crossroads, and the …
Handling large discrete action spaces via dynamic neighborhood construction
Large discrete action spaces remain a central challenge for reinforcement learning methods.
Such spaces are encountered in many real-world applications, eg, recommender systems …
Such spaces are encountered in many real-world applications, eg, recommender systems …
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning.
Existing solution approaches can handle unstructured LDAS with up to a few million actions …
Existing solution approaches can handle unstructured LDAS with up to a few million actions …